Our work is a robust counterpart of GROUSE which is very efficient for low rank subspace tracking from highly incomplete information. Though the two algorithms share the same characteristic - stochastic gradient descent on Grassmannian - GRASTA incorporates the augmented Lagrangian of l1-norm loss function into the Grassmannian optimization framework to alleviate the corruption by outliers in the subspace update at each gradient step.

As an online algorithm, GRASTA can estimate and track non-stationary subspaces when the streaming data vectors are corrupted with outliers. We apply GRASTA to the problems of robust matrix completion and real-time separation of background from foreground in video. In this second application, we show that GRASTA performs high-quality separation of moving objects from background at exceptional speeds: In one popular benchmark video example, GRASTA achieves a rate of 57 frames per second, even when run in MATLAB on a personal laptop.

We have posted our GRASTA paper at arXiv. For more detailed information please refer to our paper. If you have some questions on our work, please email us or feel free to visit our websites: Jun He and Laura Balzano.